this post was submitted on 26 Feb 2025
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Btw I didn't down vote you.
Your reply begs the question which definition of AI you are using.
I would argue that from these 8 definitions 6 apply to modern deep learning stuff. Only the category titled "Thinking Humanly" would agree with you but I personally think that these seem to be self defeating, i.e. defining AI in a way that is so dependent on humans that a machine never could have AI, which would make the word meaningless.
I'm just sick of marketing teams calling everything AI and ruining what used to be a clear goal by getting people to move the bar and compromise on what used to be rigid definitions.
I studied AI in school and am interested in it as a hobby, but these machine aren't at the point of intelligence, despite us making them feel real.
I base my personal evaluations comparing it to an autonomous being with all the attributes I described above.
ChatGPT, and other chatbots, knows what it is because it searches the web for itself, and in fact it was programmed to repeat canned responses about itself when asked because it was saying crazy shit it was finding on the internet before.
Sam Altman and many other big names in tech have admitted that we have pretty much reached the limits of what current ML models can acheive, and we basically have to reinvent a new and more efficient method of ML to keep going.
If we were to go off Alan Turing's last definition then many would argue even ChatGPT meets those definitions, but even he increased and refined his definition of AI over the years before he died.
Personally I don't think we're there yet, and by the definitons I was taught back before AI could be whatever people called it we aren't there either. I'm trying to find who specifically made the checklist for intelligencei remember, if I do I will post it here.
I am almost completely tuned out of the tech hypetrain around AI so I can't comment on the minutiae of what the various tech CEOs are claiming.
But, most of what people in the consumer tech world are seeing is entirely based on applying transformers to one kind of data: written text or images. This is only an incredibly tiny slice of the kinds of things that these transformer networks can be used for.
Even just generating a python script is incredibly impressive. If you tried to write a program that could generate arbitrary python in 2010 it would take a massive engineering effort and tens of thousands of hours of work by incredibly well educated humans. But early generations of LLMs were able to do this as an emergent behavior simply by being shown enough examples. People often fail to realize exactly how much LLMs "for free" that, previously, required a concerted effort from engineers and mathematicians.
There have been many attempts at creating programs which could predict how strings of amino acids folded into proteins. AlphaFold applied transformers to the problem and was able to predict essentially every protein that we've been able to observe. Even more, they can apply diffusion techniques (like, 'AI image generation') to generate a string of amino acids that form new novel proteins with arbitrary properties. We can write these sequences into DNA (CRISPR) and mass produce these custom designed proteins.
This is such an incredible leap in biotech that it is hard to state what kind of impact that it will have. We're already seeing things like HIV cures, optimized flu vaccines, immunotherapy drugs which are custom designed for the individual's phenotypes. We're years away from seeing the products of these technologies (clinical trials take time), but Transformers ('AI') are driving revolutionary changes in many fields.